class_docstr: Normalize a tensor image with mean and standard deviation
  
  Given mean: m and std: s
  will  normalize each channel as channel = (channel - mean) / std
  
  Args:
      mean (int): mean value
      std (int): std value
docstr: Args:
      tensor (Tensor): Tensor of stacked images or image
      of size (C, H, W) to be normalized
  
  Returns:
      Tensor: Normalized stack of image of image
docstr_tok: ["Args",":","tensor","(","Tensor",")",":","Tensor","of","stacked","images","or","image","of","size","(","C",",","H",",","W",")","to","be","normalized","Returns",":","Tensor",":","Normalized","stack","of","image","of","image"]
code: def __call__(self, tensor):
      """
      Args:
          tensor (Tensor): Tensor of stacked images or image
          of size (C, H, W) to be normalized
  
      Returns:
          Tensor: Normalized stack of image of image
      """
      return F.normalize(tensor, self.mean, self.std)
  
file_path: gluon-cv/gluoncv/torch/data/transforms/videotransforms/tensor_transforms.py
imports: import random
  from .utils import functional
label_desc: Use image node_id 2 for calling the Normalize obj's underlying member method code with example usage: obj.__call__(tensor) and returns: F